Metron develops software for NASA and industry partners providing historical and real-time anomaly detection of airline flights, as well as real-time predictions of flight and evolving airspace behaviors. We combine machine learning with the subject matter expertise of our partners.

Metron’s software helps predict and identify anomalous, risky, and inefficient flight behaviors for both real-time monitoring and an analysis of historical data to identify systemic issues.

Applied Machine Learning

Our approach combines model-driven and data-driven machine learning techniques with the subject matter expertise of our partners. We custom-design scalable time-series analytics for flight surveillance data to identify unusual flight behaviors such as unexpected flight paths, loss of separation, and overtakes. Such models are updated as “normalcy” changes with an evolving airspace. Metron’s in-house computing cluster for big data enables the efficient application of these analytics to years of historical data.

Metron Innovations

Measuring and Tracking Normalcy

Metron’s Normalcy Score Broker combines individual anomaly indicator scores—each of which measures a different aspect of the flight—into a single overall score. These scores allow system operators to identify operationally significant anomalies and can be tuned to their individual preferences for emphasizing various anomalous behaviors.

Project Timeline

Analytics for an Evolving Airspace

July 6, 2013: Asiana Airlines Crash

Asiana Airlines Flight 214 crashed on final approach into San Francisco. The pilot was flying too low and attempting a visual approach in clear weather. A Wall Street Journal analysis found that foreign air crews performed "go-arounds" at a significantly higher rate than domestic crews arriving at San Francisco under similar conditions. This finding motivated Metron to develop historical flight analysis tools that identify systemic issues to help avoid accidents.

2015-2016: Historical En Route Analysis

Metron developed a suite of anomaly indicators to analyze the "en route" (cruising altitude) flight phase. We identified historical flights that exhibited any of several anomalous or risky behaviors such as unusual routes and loss of separation. An analysis of flight metadata identified patterns within the anomalies.

2017-2019: Historical Arrivals Analysis

Metron expanded the anomaly indicator suite to analyze the "arrival" flight phase. A historical analysis identified particular carriers with patterns of unusual behaviors. Our software was integrated into NASA’s computing cluster for nightly processing of new data.

2018-2022: Real-time Arrivals & Departures Prediction

Metron is developing a novel neural network architecture to identify anomalous behaviors and make predictions of risky events in near real-time. These predictions and alerts will provide situational awareness to air traffic controllers and pilots and will enable them to implement corrective actions earlier and with less disruption.

2020-2021: Airspace Inefficiencies

Metron is developing a novel neural network architecture to jointly monitor all aircraft in the airspace to identify conditions that are likely to develop into an inefficient use of the airspace. This will provide situational awareness to those managing the airspace and identify potential issues earlier than current methods.

Our Impact

Metron’s analytic tools have identified potential systemic issues through a historical analysis of anomalies. Real-time monitoring will provide air traffic controllers, managers, and pilots with relevant alerts that reduce the required scope of corrective actions.

Core Capabilities

Our Expertise

Metron’s Advanced Data Analytics (ADA) Division led the way in the development of novel anomaly detection algorithms that efficiently scale in a distributed environment. We also advanced the state of the art of neural network architectures to enable accurate, real-time predictions. Our developed software was integrated into NASA’s systems.


Metron's analytics team develops tools to process massive amounts of data, helping our client make sense of their numbers.

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Decision Support

Metron builds decision-support systems that help operators make the best possible decision even in chaotic situations with ambiguous information.

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Key Contributor

Dr. Sean Daugherty

Machine Learning Expert

Dr. Daugherty is a Senior Research Scientist who joined Metron in 2009 and is a member of the Advanced Data Analytics division. As the principal investigator and project manager, he leads the technical team’s research and development. His proprietary research on flight analytics has advanced the state of the art of graph convolutional and recurrent neural network architectures. His algorithm development for cluster computing enables the efficient identification of anomalous flight behaviors within years of historical data.

Key Contributor

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